About The Role
We're hiring an AI Engineer for our Labs team, a fast-moving group that prototypes emerging AI/ML capabilities and takes the most promising ones to production at scale. You'll work across both traditional machine learning and generative AI, turning ideas into working demos within days and hardening them into reliable, production-grade systems. Ideal candidates are equally comfortable building a classical predictive model and architecting an LLM-powered agentic system.
Interview Process: Face-to-Face (F2F) interview in Bengaluru.
What You'll Do
- Own the complete ML lifecycle, including data curation, feature engineering, model building, evaluation, deployment, monitoring, and retraining, for both predictive ML and generative AI systems
- Build and tune classical ML models (regression, classification, ensemble methods, time series forecasting) alongside deep learning and transformer-based systems
- Build scalable pipelines for training, CI/CD, model registry, A/B testing, drift detection, and automated retraining; optimize inference for latency, throughput, and cost
- Fine-tune and deploy LLMs/SLMs using LoRA/QLoRA, PEFT, instruction tuning, and preference tuning (RLHF/DPO); apply quantization and distillation
- Design and productionize agentic systems, including RAG pipelines, tool/function calling, memory, planning loops, and multi-agent orchestration, with guardrails and observability
- Build evaluation frameworks (offline and online, LLM-as-judge, red-teaming) and traditional ML evaluation (cross-validation, ROC-AUC, precision/recall); diagnose and mitigate hallucinations, bias, and drift
- Track SOTA research across both ML and GenAI, prototype quickly, and present demos/tech talks to stakeholders and leadership
What We're Looking For
- 3+ years as an AI/ML Engineer or Applied Scientist, with proven production deployments spanning both traditional ML models and at least one LLM-based or agentic system
- Strong Python and software engineering fundamentals (version control, testing, design patterns, code review)
- Solid grounding in classical ML (feature engineering, model selection, hyperparameter tuning, ensemble methods) and a deep grasp of transformer architectures, attention, tokenization, and embeddings
- Hands-on with PyTorch, Scikit-learn, and the Hugging Face ecosystem
- Working knowledge of agentic/RAG frameworks (LangChain, LangGraph, LlamaIndex, CrewAI, or AutoGen) and vector stores (Pinecone, Weaviate, Qdrant, pgvector, or FAISS)
- Experience with inference servers (vLLM, TGI, or Triton) and quantization techniques (GPTQ, AWQ, GGUF)
- Hands-on with MLOps tools (MLflow, Weights & Biases, Airflow, or Kubeflow), Docker, Kubernetes, GPU workloads, and at least one major cloud (AWS, Azure, or GCP)
- High bias for action, strong communication, and intellectual curiosity
Education
B.Tech/M.Tech in Computer Science, Data Science, AI/ML Engineering, or a related quantitative field, or equivalent practical experience backed by a strong portfolio (open source, publications, or production work).
Nice to Have
Open-source contributions, multimodal model experience, on-device SLM deployment, or familiarity with LLM security (OWASP LLM Top 10).
Skills: ml,agentic ai,rag,ai,traditional model,llm
